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Enrica Seravalli - One of the best experts on this subject based on the ideXlab platform.

  • the trendy multi center randomized trial on hepatocellular carcinoma trial qa including automated treatment planning and Benchmark Case results
    Radiotherapy and Oncology, 2017
    Co-Authors: Steven J. M. Habraken, A.w. Sharfo, Jeroen Buijsen, Wilko F.a.r. Verbakel, Cornelis J.a. Haasbeek, Michel Öllers, Henrike Westerveld, Niek Van Wieringen, Onne Reerink, Enrica Seravalli
    Abstract:

    Background and purpose: The TRENDY trial is an international multi-center phase-II study, randomizing hepatocellular carcinoma (HCC) patients between transarterial chemoembolization (TACE) and stereotactic body radiation therapy (SBRT) with a target dose of 48-54 Gy in six fractions. The radiotherapy quality assurance (QA) program, including prospective plan feedback based on automated treatment planning, is described and results are reported. Materials and methods: Scans of a single patient were used as a Benchmark Case. Contours submitted by nine participating centers were compared with reference contours. The subsequent planning round was based on a single set of contours. A total of 20 plans from participating centers, including 12 from the Benchmark Case, 5 from a clinical pilot and 3 from the first study patients, were compared to automatically generated VMAT plans. Results: For the submitted liver contours, Dice Similarity Coefficients (DSC) with the reference delineation ranged from 0.925 to 0.954. For the GTV, the DSC varied between 0.721 and 0.876. For the 12 plans on the Benchmark Case, healthy liver normal-tissue complication probabilities (NTCPs) ranged from 0.2% to 22.2% with little correlation between NCTP and PTV-D95% (R-2 <0.3). Four protocol deviations were detected in the set of 20 treatment plans. Comparison with co-planar autoVMAT QA plans revealed these were due to too high target dose and suboptimal planning. Overall, autoVMAT resulted in an average liver NTCP reduction of 2.2 percent point (range: 16.2 percent point to -1.8 percent point, p = 0.03), and lower doses to the healthy liver (p <0.01) and gastrointestinal organs at risk (p <0.001). Conclusions: Delineation variation resulted in feedback to participating centers. Automated treatment planning can play an important role in clinical trials for prospective plan QA as suboptimal plans were detected. (c) 2017 Elsevier B.V. All rights reserved

  • The TRENDY multi-center randomized trial on hepatocellular carcinoma – Trial QA including automated treatment planning and Benchmark-Case results
    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2017
    Co-Authors: Steven J. M. Habraken, A.w. Sharfo, Jeroen Buijsen, Wilko F.a.r. Verbakel, Cornelis J.a. Haasbeek, Michel Öllers, Henrike Westerveld, Niek Van Wieringen, Onne Reerink, Enrica Seravalli
    Abstract:

    Background and purpose: The TRENDY trial is an international multi-center phase-II study, randomizing hepatocellular carcinoma (HCC) patients between transarterial chemoembolization (TACE) and stereotactic body radiation therapy (SBRT) with a target dose of 48-54 Gy in six fractions. The radiotherapy quality assurance (QA) program, including prospective plan feedback based on automated treatment planning, is described and results are reported. Materials and methods: Scans of a single patient were used as a Benchmark Case. Contours submitted by nine participating centers were compared with reference contours. The subsequent planning round was based on a single set of contours. A total of 20 plans from participating centers, including 12 from the Benchmark Case, 5 from a clinical pilot and 3 from the first study patients, were compared to automatically generated VMAT plans. Results: For the submitted liver contours, Dice Similarity Coefficients (DSC) with the reference delineation ranged from 0.925 to 0.954. For the GTV, the DSC varied between 0.721 and 0.876. For the 12 plans on the Benchmark Case, healthy liver normal-tissue complication probabilities (NTCPs) ranged from 0.2% to 22.2% with little correlation between NCTP and PTV-D95% (R-2

Denis Jose Schiozer - One of the best experts on this subject based on the ideXlab platform.

  • UNISIM-III: Benchmark Case Proposal Based on a Fractured Karst Reservoir
    ECMOR XVII, 2020
    Co-Authors: Manuel Gomes Correia, V. E. Botechia, L. O. Pires, Victor De Souza Rios, Susana M.g. Santos, J. Hohendorff, M. Chaves, Denis Jose Schiozer
    Abstract:

    Summary The significant world oil reserves related to fractured karst reservoirs in Brazilian pre-salt fields adds new frontiers to the (1) development of numerical methods for upscale giant fields with multiscale heterogeneities, (2) history matching and production strategy optimization under critical uncertainties and (3) forecast of the future reservoir performance. However, there is a lack of Benchmark models with a heterogeneous dynamic behavior typical from fractured karst reservoirs, to develop and validate novel numerical methods. This work presents a simulation Benchmark model, available as public domain data, which represents a fractured carbonate karst reservoir and add a great opportunity to test new methodologies for reservoir development and management using numerical simulation. The work structure is divided in three steps: (1) development of a reference model, a fine grid model with high level of geologic details, treated as the real field, (2) development of a simulation model under uncertainties considering an initial stage of the field development phase, and, (3) elaboration of a Benchmark proposal for studies related to the oil field development and production strategy selection. Based on the available information from well logs, several uncertainty attributes were considered in structural framework, facies and petrophysical properties. Dynamic, economic and technical uncertainties were also considered. The reference model is a giant field divided by two stratigraphic zones - the upper zone characterized by stromatolites and the lower one by coquinas. Moreover, the model is characterized by two regions with karst features near the horizons surfaces and a cluster of fractures near faults. Volcanic rocks and high permeable trends near faults are included as non-mapped uncertainties in the simulation model, as the information from well logs at the initial stage of field development does not intercept this geologic attribute. This approach will lead to several challenges on reservoir development and management. As this Benchmark is representative of a giant field, it is divided in four sectors. Sector 1 has already a production strategy defined, aiming studies regarding field management. The strategy considers WAG (water alternate gas/CO2) as recovery mechanism and the presence of 13 wells in a first wave (6 producers and 7 injectors), and other 4 wells can be added in a second wave. Field development studies can be applied in the other sectors. This Benchmark provides a great opportunity for develop and test novel numerical methods in giant reservoirs with geologic and dynamic pre-salt trends.

  • Closing the Loop Between 4D Seismic Inverted Impedance and Engineering Data: Norne Field
    81st EAGE Conference and Exhibition 2019, 2019
    Co-Authors: Masoud Maleki, Alessandra Davolio, Denis Jose Schiozer
    Abstract:

    Summary The Norne Field started production in 1997 and up to 2006 the field experienced intense production activity, making the Norne Benchmark Case an ideal candidate to explore the challenges in interpreting complex time-lapse seismic data. The objective of 4D-seismic reservoir analysis is to provide information on the dynamics of fluids and production-induced changes within the reservoir. A common alternative is to invert the seismic data and obtain acoustic impedance variations caused by production activity, and to evaluate their possible interpretations. For this Case study, a 4D-inversion scheme is used to invert the base (2001) and monitor (2006) seismic surveys in order to provide field-wide insights for the Norne Benchmark Case. We extensively interpret the observed 4D inversion anomalies and decouple, as much as possible, the effects of fluid and pressure variations, supported by production and reservoir engineering data. Moreover, we compare the inversion results with the simulation model from the Norne Benchmark Case (qualitatively and quantitatively) to update the reservoir simulation model. This research is intended as a resource to improve the quality of seismic history matching or other 4D inversion methods applied to the Norne Benchmark Case, and to demonstrate a detailed time-lapse seismic interpretation of the Norne Field.

  • Using pilot wells to integrate geological modelling and history matching: applied to the Norne Benchmark Case
    Petroleum Geoscience, 2018
    Co-Authors: Gil G. Correia, Denis Jose Schiozer
    Abstract:

    The inherent uncertainties in numerical reservoir simulation can lead to models with significant differences to observed dynamic data. History matching reduces these differences but often neglects the geological consistency of the models, compromising production forecasting reliability. To address this issue, this work proposes a geological modeling workflow integrated within a probabilistic, multi-objective history-matching workflow, using the concept of pilot points. The pilot-point method is a geostatistical parameterization technique that calibrates a pre-correlated field, generated from measured values and a set of additional synthetic data at unmeasured locations in the reservoir, referred to as pilot points. In this study, the synthetic data corresponds to synthetic wells, henceforth referred to as pilot wells. The methodology is applied to a real dataset, the Norne field Benchmark Case. The flexibility of the pilot-well method is the principal advantage, while a key challenge is to optimize the pilot-well configuration. The configuration includes production data, the preferred fluid flow paths, and the geological framework. The flexibility of the method is demonstrated in the two Case studies presented here: generating specific sedimentary features (G-segment) and finding the best location for the cemented stringers responsible for the fluid behavior (C-segment).

  • Investigation of production forecast biases of simulation models in a Benchmark Case
    Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, 2018
    Co-Authors: Vinicius Eduardo Botechia, Guilherme Daniel Avansi, Alessandra Davolio, Ana Teresa Ferreira Da Silva Gaspar, Denis Jose Schiozer
    Abstract:

    Reservoir management decisions are often based on simulation models and probabilistic approaches. Thus, the response of the model must be sufficiently accurate to base sound decisions on and fast enough to be practical for methodologies requiring many simulation runs. However, simulation models often forecast production rates different to real production rates for various reasons. Two possible causes of these deviations are (1) upscaling (a technique to reduce the computational time of simulation models by reducing the number of grid blocks) and (2) uncertainties (the values established to attributes are different from real values caused by lack of knowledge of real reservoir). Morosov and Schiozer [(2016) applied a closed-loop technique in a Benchmark Case where decisions taken using the simulation models are applied to a reference Case. The optimized production strategy, using simulations models, increased the expected monetary value of the project by about 29%, but the Net Present Value (NPV), calculated using a reference Case, decreased by 2%. The real NPV was outside the expected range and revealed that the set of models did not fully represent the real field, even for high-quality history-matched models. The objective of this study is to identify the causes of these discrepancies. To reach this goal, we investigate and analyze both the impact of the upscaling and the uncertainty on production and economic indicators. We use a set of representative models of Benchmark UNISIM-I (Avansi and Schiozer, 2015) to consider the effects of uncertainty and upscaling. Our main concern was the uncertainties in the distribution of petrophysical properties that strongly influence the productivity and injectivity of wells, noted by Morosov and Schiozer (2016) as being the main cause for differences among models. Furthermore, to verify the isolated effects of the possible causes of deviation, we use a single model to show only the effects of upscaling, and another set of models showing only the uncertainty. The results showed that the impact of the uncertainties was higher than the upscaling for the studied Case. The upscaling generated an optimistic bias for production and economic indicators, but well-correlated with the reference Case. The uncertainties significantly affected the production forecasts for this study. This happened because the response of the wells is highly dependent on the petrophysical properties of the model, which varies widely between the different models representing uncertainties and was not adequately depicted by the representative models.

  • Estimating the chance of success of information acquisition for the Norne Benchmark Case
    Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, 2018
    Co-Authors: Vinicius Eduardo Botechia, Ana Teresa Ferreira Da Silva Gaspar, Daniel Rodrigues Dos Santos, Carlos Eduardo Andrade Barreto, Susana Margarida Da Graça Santos, Denis Jose Schiozer
    Abstract:

    A key decision in field management is whether or not to acquire information to either improve project economics or reduce uncertainties. A widely spread technique to quantify the gain of information acquisition is Value of Information (VoI). However, estimating the possible outcomes of future information without the data is a complex task. While traditional VoI estimates are based on a single average value, the Chance of Success (CoS) methodology works as a diagnostic tool, estimating a range of possible outcomes that vary because of reservoir uncertainties. The objective of this work is to estimate the CoS of a 4D seismic before having the data, applied to a complex real Case (Norne field). The objective is to assist the decision of whether, or not, to acquire further data. The methodology comprises the following steps: uncertainty quantification, selection of Representative Models (RMs), estimation of the acquisition period, production strategy optimization and, finally, quantification of the CoS. The estimates use numerical reservoir simulation, economic analysis, and uncertainty evaluation. We performed analyses considering perfect and imperfect information. We aim to verify the increment in economic return when the 4D data identifies the closest-to-reality reservoir model. While the traditional expected VoI calculation provides only an average value, this methodology has the advantage of considering the increase in the economic return due to reservoir uncertainties, characterized by different RMs. Our results showed that decreased reliability of information affected the decision of which production strategy to select. In our Case, information reliability less than 70% is insufficient to change the perception of the uncertain reservoir and consequently decisions. Furthermore, when the reliability reached around 50%, the information lost value, as the economic return became similar to that of the Case without information acquisition.

Ramin Sedaghati - One of the best experts on this subject based on the ideXlab platform.

  • Benchmark Case studies in optimization of geometrically nonlinear structures
    Structural and Multidisciplinary Optimization, 2005
    Co-Authors: Afzal Suleman, Ramin Sedaghati
    Abstract:

    A structural optimization algorithm is developed for truss and beam structures undergoing large deflections against instability. The method combines the nonlinear buckling analysis using the displacement control technique, with the optimality criteria approaches. Several Benchmark Case studies illustrate the procedure and the results are compared with examples reported in the literature. It is shown that a design based on the generalized eigenvalue problem (linear buckling) highly underestimates the optimum mass or overestimates the buckling load for these types of structures, so a design based on the linear buckling analysis may result in catastrophic failure. The effect of geometrical nonlinearities and element imperfections has also been studied.

  • Benchmark Case studies in structural design optimization using the force method
    International Journal of Solids and Structures, 2005
    Co-Authors: Ramin Sedaghati
    Abstract:

    Abstract A structural optimization algorithm is developed for truss and beam structures under stress–displacement or frequency constraints. The algorithm combines the mathematical programming based on the Sequential Quadratic Programming (SQP) technique and the finite element technique based on the Integrated Force Method. A new approach based on the single value decomposition technique has been developed to derive the compatibility matrix required in the force method. Benchmark Case studies illustrate the procedure and allow the results obtained to be compared with those reported in the literature. It is shown that the computational effort required by the force method is significantly lower than that of the displacement method and in some Cases such as structural optimization problems with multiple frequency constraints, the analysis procedure (force or displacement method) significantly affects the final optimum design and the structural optimization based on the force method may result in a lighter design.

Geir Naevdal - One of the best experts on this subject based on the ideXlab platform.

  • efficient big data assimilation through sparse representation a 3d Benchmark Case study in petroleum engineering
    PLOS ONE, 2018
    Co-Authors: Xiaodong Luo, Tuhin Bhakta, Morten Jakobsen, Geir Naevdal
    Abstract:

    Data assimilation is an important discipline in geosciences that aims to combine the information contents from both prior geophysical models and observational data (observations) to obtain improved model estimates. Ensemble-based methods are among the state-of-the-art assimilation algorithms in the data assimilation community. When applying ensemble-based methods to assimilate big geophysical data, substantial computational resources are needed in order to compute and/or store certain quantities (e.g., the Kalman-gain-type matrix), given both big model and data sizes. In addition, uncertainty quantification of observational data, e.g., in terms of estimating the observation error covariance matrix, also becomes computationally challenging, if not infeasible. To tackle the aforementioned challenges in the presence of big data, in a previous study, the authors proposed a wavelet-based sparse representation procedure for 2D seismic data assimilation problems (also known as history matching problems in petroleum engineering). In the current study, we extend the sparse representation procedure to 3D problems, as this is an important step towards real field Case studies. To demonstrate the efficiency of the extended sparse representation procedure, we apply an ensemble-based seismic history matching framework with the extended sparse representation procedure to a 3D Benchmark Case, the Brugge field. In this Benchmark Case study, the total number of seismic data is in the order of [Formula: see text]. We show that the wavelet-based sparse representation procedure is extremely efficient in reducing the size of seismic data, while preserving the salient features of seismic data. Moreover, even with a substantial data-size reduction through sparse representation, the ensemble-based seismic history matching framework can still achieve good estimation accuracy.

  • efficient big data assimilation through sparse representation a 3d Benchmark Case study in seismic history matching
    arXiv: Data Analysis Statistics and Probability, 2016
    Co-Authors: Xiaodong Luo, Tuhin Bhakta, Morten Jakobsen, Geir Naevdal
    Abstract:

    In a previous work \citep{luo2016sparse2d_spej}, the authors proposed an ensemble-based 4D seismic history matching (SHM) framework, which has some relatively new ingredients, in terms of the type of seismic data in choice, the way to handle big seismic data and related data noise estimation, and the use of a recently developed iterative ensemble history matching algorithm. In seismic history matching, it is customary to use inverted seismic attributes, such as acoustic impedance, as the observed data. In doing so, extra uncertainties may arise during the inversion processes. The proposed SHM framework avoids such intermediate inversion processes by adopting amplitude versus angle (AVA) data. In addition, SHM typically involves assimilating a large amount of observed seismic attributes into reservoir models. To handle the big-data problem in SHM, the proposed framework adopts the following wavelet-based sparse representation procedure: First, a discrete wavelet transform is applied to observed seismic attributes. Then, uncertainty analysis is conducted in the wavelet domain to estimate noise in the resulting wavelet coefficients, and to calculate a corresponding threshold value. Wavelet coefficients above the threshold value, called leading wavelet coefficients hereafter, are used as the data for history matching. The retained leading wavelet coefficients preserve the most salient features of the observed seismic attributes, whereas rendering a substantially smaller data size. Finally, an iterative ensemble smoother is adopted to update reservoir models, in such a way that the leading wavelet coefficients of simulated seismic attributes better match those of observed seismic attributes. (The rest of the abstract was omitted for the length restriction.)

Steven J. M. Habraken - One of the best experts on this subject based on the ideXlab platform.

  • the trendy multi center randomized trial on hepatocellular carcinoma trial qa including automated treatment planning and Benchmark Case results
    Radiotherapy and Oncology, 2017
    Co-Authors: Steven J. M. Habraken, A.w. Sharfo, Jeroen Buijsen, Wilko F.a.r. Verbakel, Cornelis J.a. Haasbeek, Michel Öllers, Henrike Westerveld, Niek Van Wieringen, Onne Reerink, Enrica Seravalli
    Abstract:

    Background and purpose: The TRENDY trial is an international multi-center phase-II study, randomizing hepatocellular carcinoma (HCC) patients between transarterial chemoembolization (TACE) and stereotactic body radiation therapy (SBRT) with a target dose of 48-54 Gy in six fractions. The radiotherapy quality assurance (QA) program, including prospective plan feedback based on automated treatment planning, is described and results are reported. Materials and methods: Scans of a single patient were used as a Benchmark Case. Contours submitted by nine participating centers were compared with reference contours. The subsequent planning round was based on a single set of contours. A total of 20 plans from participating centers, including 12 from the Benchmark Case, 5 from a clinical pilot and 3 from the first study patients, were compared to automatically generated VMAT plans. Results: For the submitted liver contours, Dice Similarity Coefficients (DSC) with the reference delineation ranged from 0.925 to 0.954. For the GTV, the DSC varied between 0.721 and 0.876. For the 12 plans on the Benchmark Case, healthy liver normal-tissue complication probabilities (NTCPs) ranged from 0.2% to 22.2% with little correlation between NCTP and PTV-D95% (R-2 <0.3). Four protocol deviations were detected in the set of 20 treatment plans. Comparison with co-planar autoVMAT QA plans revealed these were due to too high target dose and suboptimal planning. Overall, autoVMAT resulted in an average liver NTCP reduction of 2.2 percent point (range: 16.2 percent point to -1.8 percent point, p = 0.03), and lower doses to the healthy liver (p <0.01) and gastrointestinal organs at risk (p <0.001). Conclusions: Delineation variation resulted in feedback to participating centers. Automated treatment planning can play an important role in clinical trials for prospective plan QA as suboptimal plans were detected. (c) 2017 Elsevier B.V. All rights reserved

  • The TRENDY multi-center randomized trial on hepatocellular carcinoma – Trial QA including automated treatment planning and Benchmark-Case results
    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2017
    Co-Authors: Steven J. M. Habraken, A.w. Sharfo, Jeroen Buijsen, Wilko F.a.r. Verbakel, Cornelis J.a. Haasbeek, Michel Öllers, Henrike Westerveld, Niek Van Wieringen, Onne Reerink, Enrica Seravalli
    Abstract:

    Background and purpose: The TRENDY trial is an international multi-center phase-II study, randomizing hepatocellular carcinoma (HCC) patients between transarterial chemoembolization (TACE) and stereotactic body radiation therapy (SBRT) with a target dose of 48-54 Gy in six fractions. The radiotherapy quality assurance (QA) program, including prospective plan feedback based on automated treatment planning, is described and results are reported. Materials and methods: Scans of a single patient were used as a Benchmark Case. Contours submitted by nine participating centers were compared with reference contours. The subsequent planning round was based on a single set of contours. A total of 20 plans from participating centers, including 12 from the Benchmark Case, 5 from a clinical pilot and 3 from the first study patients, were compared to automatically generated VMAT plans. Results: For the submitted liver contours, Dice Similarity Coefficients (DSC) with the reference delineation ranged from 0.925 to 0.954. For the GTV, the DSC varied between 0.721 and 0.876. For the 12 plans on the Benchmark Case, healthy liver normal-tissue complication probabilities (NTCPs) ranged from 0.2% to 22.2% with little correlation between NCTP and PTV-D95% (R-2